This case study describes how a customer validated the use of AI in formulation development by comparing traditional experimentation with AI‑guided experimentation using the Citrine Platform. Starting with 100 existing formulations, the team conducted 200 experiments through conventional methods and 200 suggested by the AI. Citrine automatically enriched each formulation with additional chemical data and applied sequential learning, where experimental results continuously retrained the model to refine future recommendations. By monitoring the platform’s feature importance insights, the team ensured the AI was effectively learning and identifying key predictors, demonstrating improved efficiency and confidence in data‑driven experimentation.

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